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Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

Author

Listed:
  • Zhongyong Zhao

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Chao Tang

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Qu Zhou

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Lingna Xu

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Yingang Gui

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Chenguo Yao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

Suggested Citation

  • Zhongyong Zhao & Chao Tang & Qu Zhou & Lingna Xu & Yingang Gui & Chenguo Yao, 2017. "Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine," Energies, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2022-:d:121182
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    References listed on IDEAS

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    1. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    2. Byung Eun Lee & Jung-Wook Park & Peter A. Crossley & Yong Cheol Kang, 2014. "Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer," Energies, MDPI, vol. 7(9), pages 1-19, September.
    3. Saleh Alsuhaibani & Yasin Khan & Abderrahmane Beroual & Nazar Hussain Malik, 2016. "A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics," Energies, MDPI, vol. 9(11), pages 1-17, October.
    4. Qing Yang & Peiyu Su & Yong Chen, 2017. "Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults," Energies, MDPI, vol. 10(4), pages 1-16, March.
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    Citations

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    Cited by:

    1. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    2. Javier Leiva & Rubén Carmona Pardo & José A. Aguado, 2019. "Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks," Energies, MDPI, vol. 12(7), pages 1-20, April.
    3. Omid Elahi & Reza Behkam & Gevork B. Gharehpetian & Fazel Mohammadi, 2022. "Diagnosing Disk-Space Variation in Distribution Power Transformer Windings Using Group Method of Data Handling Artificial Neural Networks," Energies, MDPI, vol. 15(23), pages 1-32, November.

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